Documentation Index
Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
Use this file to discover all available pages before exploring further.
LLM Module
The LLM module provides the core language model client and utilities for agent interactions, supporting multiple providers through LiteLLM.
Param Cluster Map
| Cluster | Legacy Params | Consolidated To | Status |
|---|
| LLM | llm, llm_config, function_calling_llm | llm= | ✅ Done |
base_url and api_key remain separate parameters (connection/auth constraint).
Precedence Ladder
Instance > Config > Array > Dict > String > Bool > Default
Ways to Use llm=
| Form | Example | Description |
|---|
| String | llm="gpt-4o" | Model name |
| Alias | model="gpt-4o" | Same as llm= |
| Dict | llm={"model": "gpt-4o", "temperature": 0.7} | Config dict |
| Instance | llm=LLM(model="gpt-4o") | LLM instance |
Legacy (Deprecated)
# ⚠️ DEPRECATED - still works but emits warning
Agent(llm={"temperature": 0.7})
Agent(function_calling_llm="gpt-4o-mini")
# ✅ NEW - use llm= instead
Agent(llm="gpt-4o")
Agent(model="gpt-4o") # alias
Installation
pip install praisonaiagents
Quick Start
from praisonaiagents.llm import LLM
llm = LLM(model="gpt-4o-mini")
response = llm.chat("What is machine learning?")
print(response)
Classes
LLM
Main LLM client class supporting multiple providers.
from praisonaiagents.llm import LLM
llm = LLM(
model="gpt-4o-mini",
temperature=0.7,
max_tokens=1000
)
Constructor
| Parameter | Type | Default | Description |
|---|
model | str | "gpt-4o-mini" | Model identifier |
temperature | float | 0.7 | Sampling temperature |
max_tokens | int | None | Maximum tokens in response |
api_key | str | None | API key (uses env var if not set) |
base_url | str | None | Custom API base URL |
Methods
| Method | Description |
|---|
chat(prompt, **kwargs) | Send a chat message |
chat_async(prompt, **kwargs) | Async chat message |
stream(prompt, **kwargs) | Stream response |
get_response(messages, **kwargs) | Get response from messages |
LLMContextLengthExceededException
Exception raised when context length is exceeded.
from praisonaiagents.llm import LLMContextLengthExceededException
try:
response = llm.chat(very_long_prompt)
except LLMContextLengthExceededException as e:
print(f"Context too long: {e}")
OpenAIClient
OpenAI-compatible client for direct API access.
from praisonaiagents.llm import OpenAIClient, get_openai_client
client = get_openai_client(model="gpt-4o-mini")
response = client.chat.completions.create(
messages=[{"role": "user", "content": "Hello"}]
)
ModelRouter
Intelligent model routing based on task complexity.
from praisonaiagents.llm import ModelRouter, TaskComplexity
router = ModelRouter(
models={
TaskComplexity.LOW: "gpt-4o-mini",
TaskComplexity.MEDIUM: "gpt-4o",
TaskComplexity.HIGH: "gpt-4-turbo"
}
)
model = router.select(task="Simple greeting")
TaskComplexity
| Level | Description |
|---|
LOW | Simple tasks, quick responses |
MEDIUM | Moderate complexity |
HIGH | Complex reasoning, long context |
ModelProfile
Profile for a model’s capabilities.
from praisonaiagents.llm import ModelProfile
profile = ModelProfile(
name="gpt-4o-mini",
context_length=128000,
supports_tools=True,
supports_vision=True,
cost_per_1k_input=0.00015,
cost_per_1k_output=0.0006
)
Response Types
ChatCompletion
from praisonaiagents.llm import ChatCompletion
# Response structure
completion.id # Completion ID
completion.choices # List of choices
completion.usage # Token usage
completion.model # Model used
ChatCompletionMessage
from praisonaiagents.llm import ChatCompletionMessage
message.role # "assistant", "user", etc.
message.content # Message content
message.tool_calls # Tool calls if any
from praisonaiagents.llm import ToolCall
tool_call.id # Tool call ID
tool_call.type # "function"
tool_call.function # Function details
CompletionUsage
from praisonaiagents.llm import CompletionUsage
usage.prompt_tokens # Input tokens
usage.completion_tokens # Output tokens
usage.total_tokens # Total tokens
Utility Functions
supports_structured_outputs
Check if a model supports structured outputs.
from praisonaiagents.llm import supports_structured_outputs
if supports_structured_outputs("gpt-4o-mini"):
# Use structured output mode
pass
from praisonaiagents.llm import supports_streaming_with_tools
if supports_streaming_with_tools("gpt-4o"):
# Enable streaming with tools
pass
process_stream_chunks
Process streaming response chunks.
from praisonaiagents.llm import process_stream_chunks
for chunk in process_stream_chunks(stream_response):
print(chunk.content, end="")
create_routing_agent
Create an agent with model routing.
from praisonaiagents.llm import create_routing_agent
agent = create_routing_agent(
name="Smart Agent",
router=model_router
)
Usage Examples
Basic Chat
from praisonaiagents.llm import LLM
llm = LLM(model="gpt-4o-mini")
response = llm.chat("Explain quantum computing in simple terms")
print(response)
Streaming Response
from praisonaiagents.llm import LLM
llm = LLM(model="gpt-4o-mini")
for chunk in llm.stream("Write a short story"):
print(chunk, end="", flush=True)
from praisonaiagents.llm import LLM
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"}
},
"required": ["location"]
}
}
}
]
llm = LLM(model="gpt-4o-mini")
response = llm.chat(
"What's the weather in Tokyo?",
tools=tools
)
Model Routing
from praisonaiagents.llm import ModelRouter, TaskComplexity
router = ModelRouter(
models={
TaskComplexity.LOW: "gpt-4o-mini",
TaskComplexity.MEDIUM: "gpt-4o",
TaskComplexity.HIGH: "claude-3-opus"
}
)
# Router automatically selects appropriate model
model = router.select(task="Complex mathematical proof")
llm = LLM(model=model)
Different Providers
from praisonaiagents.llm import LLM
# OpenAI
openai_llm = LLM(model="gpt-4o-mini")
# Anthropic
claude_llm = LLM(model="claude-3-sonnet-20240229")
# Ollama (local)
ollama_llm = LLM(model="ollama/llama3")
# Google
gemini_llm = LLM(model="gemini/gemini-pro")
# Groq
groq_llm = LLM(model="groq/llama3-70b-8192")
Environment Variables
| Variable | Description |
|---|
OPENAI_API_KEY | OpenAI API key |
ANTHROPIC_API_KEY | Anthropic API key |
GOOGLE_API_KEY | Google AI API key |
GROQ_API_KEY | Groq API key |
OPENROUTER_API_KEY | OpenRouter API key |
